CrypTen: Secure Multi-Party Computation Meets Machine Learning

• URL: http://arxiv.org/abs/2109.00984v1
• Date: Thu, 2 Sep 2021 14:36:55 GMT
• Title: CrypTen: Secure Multi-Party Computation Meets Machine Learning
• Authors: Brian Knott and Shobha Venkataraman and Awni Hannun and Shubho Sengupta and Mark Ibrahim and Laurens van der Maaten
• Abstract summary: CrypTen is a software framework that exposes popular secure MPC primitives via abstractions common in modern machine-learning frameworks. This paper describes the design of CrypTen and measure its performance on state-of-the-art models for text classification, speech recognition, and image classification.
• Score: 25.21435023269728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it facilitates training of machine-learning models on private data sets owned by different parties, evaluation of one party's private model using another party's private data, etc. Although a range of studies implement machine-learning models via secure MPC, such implementations are not yet mainstream. Adoption of secure MPC is hampered by the absence of flexible software frameworks that "speak the language" of machine-learning researchers and engineers. To foster adoption of secure MPC in machine learning, we present CrypTen: a software framework that exposes popular secure MPC primitives via abstractions that are common in modern machine-learning frameworks, such as tensor computations, automatic differentiation, and modular neural networks. This paper describes the design of CrypTen and measure its performance on state-of-the-art models for text classification, speech recognition, and image classification. Our benchmarks show that CrypTen's GPU support and high-performance communication between (an arbitrary number of) parties allows it to perform efficient private evaluation of modern machine-learning models under a semi-honest threat model. For example, two parties using CrypTen can securely predict phonemes in speech recordings using Wav2Letter faster than real-time. We hope that CrypTen will spur adoption of secure MPC in the machine-learning community.

Related papers

• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing [51.336015600778396]
  Federated Learning (FL) has gained lots of traction recently, both in industry and academia. In FL, a machine learning model is trained using data from various end-users arranged in committees across several rounds. Since such data can often be sensitive, a primary challenge in FL is providing privacy while still retaining utility of the model.
  arXiv  Detail & Related papers  (2024-10-21T16:25:14Z)
• Privacy Side Channels in Machine Learning Systems [87.53240071195168]
  We introduce privacy side channels: attacks that exploit system-level components to extract private information. For example, we show that deduplicating training data before applying differentially-private training creates a side-channel that completely invalidates any provable privacy guarantees. We further show that systems which block language models from regenerating training data can be exploited to exfiltrate private keys contained in the training set.
  arXiv  Detail & Related papers  (2023-09-11T16:49:05Z)
• Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM [2.144835105990896]
  We develop an end-to-end code generator parsing a pre-trained model to C source libraries for the backend. Specific compute-intensive operators can be easily offloaded to the dedicated accelerator. We conduct a hand gesture recognition experiment on an ARM Cortex M4F core.
  arXiv  Detail & Related papers  (2023-04-10T19:56:48Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings [57.45332961252628]
  Privacy-preserving machine learning in data-sharing processes is an ever-critical task. This paper presents an innovative framework that uses Representation Learning via autoencoders to generate privacy-preserving embedded data.
  arXiv  Detail & Related papers  (2022-11-10T17:36:58Z)
• Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors [68.8204255655161]
  This paper compares four state-of-the-art algorithms in two real applications: gesture recognition based on accelerometer data and image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs.
  arXiv  Detail & Related papers  (2022-09-01T17:05:20Z)
• MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning [5.203329540700177]
  This thesis focuses on designing efficient MPC frameworks for 2, 3 and 4 parties, with at most one corruption and supports ring structures. We propose two variants for each of our frameworks, with one variant aiming to minimise the execution time while the other focuses on the monetary cost.
  arXiv  Detail & Related papers  (2021-12-26T09:25:32Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• MORSE-STF: A Privacy Preserving Computation System [12.875477499515158]
  We present Secure-TF, a privacy-preserving machine learning framework based on MPC. Our framework is able to support widely-used machine learning models such as logistic regression, fully-connected neural network, and convolutional neural network.
  arXiv  Detail & Related papers  (2021-09-24T03:42:46Z)
• TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption [0.0]
  We present TenSEAL, an open-source library for Privacy-Preserving Machine Learning using Homomorphic Encryption. We show that an encrypted convolutional neural network can be evaluated in less than a second, using less than half a megabyte of communication.
  arXiv  Detail & Related papers  (2021-04-07T14:32:38Z)
• Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support [1.5362025549031049]
  Perun is a framework for confidential multi-stakeholder machine learning. It executes ML training on hardware accelerators (e.g., GPU) while providing security guarantees. During the ML training on CIFAR-10 and real-world medical datasets, Perun achieved a 161x to 1560x speedup.
  arXiv  Detail & Related papers  (2021-03-31T08:31:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.